Literatura académica sobre el tema "ENSEMBLE LEARNING MODELS"
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Artículos de revistas sobre el tema "ENSEMBLE LEARNING MODELS"
GURBYCH, A. "METHOD SUPER LEARNING FOR DETERMINATION OF MOLECULAR RELATIONSHIP". Herald of Khmelnytskyi National University. Technical sciences 307, n.º 2 (2 de mayo de 2022): 14–24. http://dx.doi.org/10.31891/2307-5732-2022-307-2-14-24.
Texto completoACOSTA-MENDOZA, NIUSVEL, ALICIA MORALES-REYES, HUGO JAIR ESCALANTE y ANDRÉS GAGO-ALONSO. "LEARNING TO ASSEMBLE CLASSIFIERS VIA GENETIC PROGRAMMING". International Journal of Pattern Recognition and Artificial Intelligence 28, n.º 07 (14 de octubre de 2014): 1460005. http://dx.doi.org/10.1142/s0218001414600052.
Texto completoSiswoyo, Bambang, Zuraida Abal Abas, Ahmad Naim Che Pee, Rita Komalasari y Nano Suryana. "Ensemble machine learning algorithm optimization of bankruptcy prediction of bank". IAES International Journal of Artificial Intelligence (IJ-AI) 11, n.º 2 (1 de junio de 2022): 679. http://dx.doi.org/10.11591/ijai.v11.i2.pp679-686.
Texto completoHuang, Haifeng, Lei Huang, Rongjia Song, Feng Jiao y Tao Ai. "Bus Single-Trip Time Prediction Based on Ensemble Learning". Computational Intelligence and Neuroscience 2022 (11 de agosto de 2022): 1–24. http://dx.doi.org/10.1155/2022/6831167.
Texto completoRuaud, Albane, Niklas Pfister, Ruth E. Ley y Nicholas D. Youngblut. "Interpreting tree ensemble machine learning models with endoR". PLOS Computational Biology 18, n.º 12 (14 de diciembre de 2022): e1010714. http://dx.doi.org/10.1371/journal.pcbi.1010714.
Texto completoKhanna, Samarth y Kabir Nagpal. "Sign Language Interpretation using Ensembled Deep Learning Models". ITM Web of Conferences 53 (2023): 01003. http://dx.doi.org/10.1051/itmconf/20235301003.
Texto completoAlazba, Amal y Hamoud Aljamaan. "Software Defect Prediction Using Stacking Generalization of Optimized Tree-Based Ensembles". Applied Sciences 12, n.º 9 (30 de abril de 2022): 4577. http://dx.doi.org/10.3390/app12094577.
Texto completoSonawane, Deepkanchan Nanasaheb. "Ensemble Learning For Increasing Accuracy Data Models". IOSR Journal of Computer Engineering 9, n.º 1 (2013): 35–37. http://dx.doi.org/10.9790/0661-0913537.
Texto completoLi, Ziyue, Kan Ren, Yifan Yang, Xinyang Jiang, Yuqing Yang y Dongsheng Li. "Towards Inference Efficient Deep Ensemble Learning". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 7 (26 de junio de 2023): 8711–19. http://dx.doi.org/10.1609/aaai.v37i7.26048.
Texto completoAbdillah, Abid Famasya, Cornelius Bagus Purnama Putra, Apriantoni Apriantoni, Safitri Juanita y Diana Purwitasari. "Ensemble-based Methods for Multi-label Classification on Biomedical Question-Answer Data". Journal of Information Systems Engineering and Business Intelligence 8, n.º 1 (26 de abril de 2022): 42–50. http://dx.doi.org/10.20473/jisebi.8.1.42-50.
Texto completoTesis sobre el tema "ENSEMBLE LEARNING MODELS"
He, Wenbin. "Exploration and Analysis of Ensemble Datasets with Statistical and Deep Learning Models". The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1574695259847734.
Texto completoKim, Jinhan. "J-model : an open and social ensemble learning architecture for classification". Thesis, University of Edinburgh, 2012. http://hdl.handle.net/1842/7672.
Texto completoGharroudi, Ouadie. "Ensemble multi-label learning in supervised and semi-supervised settings". Thesis, Lyon, 2017. http://www.theses.fr/2017LYSE1333/document.
Texto completoMulti-label learning is a specific supervised learning problem where each instance can be associated with multiple target labels simultaneously. Multi-label learning is ubiquitous in machine learning and arises naturally in many real-world applications such as document classification, automatic music tagging and image annotation. In this thesis, we formulate the multi-label learning as an ensemble learning problem in order to provide satisfactory solutions for both the multi-label classification and the feature selection tasks, while being consistent with respect to any type of objective loss function. We first discuss why the state-of-the art single multi-label algorithms using an effective committee of multi-label models suffer from certain practical drawbacks. We then propose a novel strategy to build and aggregate k-labelsets based committee in the context of ensemble multi-label classification. We then analyze the effect of the aggregation step within ensemble multi-label approaches in depth and investigate how this aggregation impacts the prediction performances with respect to the objective multi-label loss metric. We then address the specific problem of identifying relevant subsets of features - among potentially irrelevant and redundant features - in the multi-label context based on the ensemble paradigm. Three wrapper multi-label feature selection methods based on the Random Forest paradigm are proposed. These methods differ in the way they consider label dependence within the feature selection process. Finally, we extend the multi-label classification and feature selection problems to the semi-supervised setting and consider the situation where only few labelled instances are available. We propose a new semi-supervised multi-label feature selection approach based on the ensemble paradigm. The proposed model combines ideas from co-training and multi-label k-labelsets committee construction in tandem with an inner out-of-bag label feature importance evaluation. Satisfactorily tested on several benchmark data, the approaches developed in this thesis show promise for a variety of applications in supervised and semi-supervised multi-label learning
Henriksson, Aron. "Ensembles of Semantic Spaces : On Combining Models of Distributional Semantics with Applications in Healthcare". Doctoral thesis, Stockholms universitet, Institutionen för data- och systemvetenskap, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-122465.
Texto completoAt the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 4 and 5: Unpublished conference papers.
High-Performance Data Mining for Drug Effect Detection
Chakraborty, Debaditya. "Detection of Faults in HVAC Systems using Tree-based Ensemble Models and Dynamic Thresholds". University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1543582336141076.
Texto completoLi, Qiongzhu. "Study of Single and Ensemble Machine Learning Models on Credit Data to Detect Underlying Non-performing Loans". Thesis, Uppsala universitet, Statistiska institutionen, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-297080.
Texto completoFranch, Gabriele. "Deep Learning for Spatiotemporal Nowcasting". Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/295096.
Texto completoFranch, Gabriele. "Deep Learning for Spatiotemporal Nowcasting". Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/295096.
Texto completoEkström, Linus y Andreas Augustsson. "A comperative study of text classification models on invoices : The feasibility of different machine learning algorithms and their accuracy". Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-15647.
Texto completoLundberg, Jacob. "Resource Efficient Representation of Machine Learning Models : investigating optimization options for decision trees in embedded systems". Thesis, Linköpings universitet, Statistik och maskininlärning, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-162013.
Texto completoLibros sobre el tema "ENSEMBLE LEARNING MODELS"
Kyriakides, George y Konstantinos G. Margaritis. Hands-On Ensemble Learning with Python: Build Highly Optimized Ensemble Machine Learning Models Using Scikit-Learn and Keras. Packt Publishing, Limited, 2019.
Buscar texto completoHead, Paul D. The Choral Experience. Editado por Frank Abrahams y Paul D. Head. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199373369.013.3.
Texto completoSummerson, Samantha R. y Caleb Kemere. Multi-electrode Recording of Neural Activity in Awake Behaving Animals. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199939800.003.0004.
Texto completoWheelahan, Leesa. Rethinking Skills Development. Editado por John Buchanan, David Finegold, Ken Mayhew y Chris Warhurst. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199655366.013.30.
Texto completoCapítulos de libros sobre el tema "ENSEMBLE LEARNING MODELS"
Coqueret, Guillaume y Tony Guida. "Ensemble models". En Machine Learning for Factor Investing, 173–86. Boca Raton: Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003121596-14.
Texto completoKumar, Alok y Mayank Jain. "Mixing Models". En Ensemble Learning for AI Developers, 31–48. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5940-5_3.
Texto completoBisong, Ekaba. "Ensemble Methods". En Building Machine Learning and Deep Learning Models on Google Cloud Platform, 269–86. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4470-8_23.
Texto completoHennicker, Rolf, Alexander Knapp y Martin Wirsing. "Epistemic Ensembles". En Leveraging Applications of Formal Methods, Verification and Validation. Adaptation and Learning, 110–26. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19759-8_8.
Texto completoJuniper, Matthew P. "Machine Learning for Thermoacoustics". En Lecture Notes in Energy, 307–37. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-16248-0_11.
Texto completoBrazdil, Pavel, Jan N. van Rijn, Carlos Soares y Joaquin Vanschoren. "Metalearning in Ensemble Methods". En Metalearning, 189–200. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-67024-5_10.
Texto completoDritsas, Elias, Maria Trigka y Phivos Mylonas. "Ensemble Machine Learning Models for Breast Cancer Identification". En IFIP Advances in Information and Communication Technology, 303–11. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-34171-7_24.
Texto completoDi Napoli, Mariano, Giuseppe Bausilio, Andrea Cevasco, Pierluigi Confuorto, Andrea Mandarino y Domenico Calcaterra. "Landslide Susceptibility Assessment by Ensemble-Based Machine Learning Models". En Understanding and Reducing Landslide Disaster Risk, 225–31. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60227-7_24.
Texto completoMokeev, Vladimir. "An Ensemble of Learning Machine Models for Plant Recognition". En Communications in Computer and Information Science, 256–62. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39575-9_26.
Texto completoSingh, Divjot y Ashutosh Mishra. "Early Prediction of Alzheimer’s Disease Using Ensemble Learning Models". En Springer Proceedings in Mathematics & Statistics, 459–77. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-15175-0_38.
Texto completoActas de conferencias sobre el tema "ENSEMBLE LEARNING MODELS"
Celikyilmaz, Asli y Dilek Hakkani-Tur. "Investigation of ensemble models for sequence learning". En ICASSP 2015 - 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2015. http://dx.doi.org/10.1109/icassp.2015.7178999.
Texto completoKordik, Pavel y Jan Cerny. "Building predictive models in two stages with meta-learning templates optimized by genetic programming". En 2014 IEEE Symposium on Computational Intelligence in Ensemble Learning (CIEL). IEEE, 2014. http://dx.doi.org/10.1109/ciel.2014.7015740.
Texto completoKotary, James, Vincenzo Di Vito y Ferdinando Fioretto. "Differentiable Model Selection for Ensemble Learning". En Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/217.
Texto completoK P, Saranyanath, Wei Shi y Jean-Pierre Corriveau. "Cyberbullying Detection using Ensemble Method". En 3rd International Conference on Data Science and Machine Learning (DSML 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.121507.
Texto completoCheung, Catherine y Zouhair Hamaimou. "Ensemble Integration Methods for Load Estimation". En Vertical Flight Society 78th Annual Forum & Technology Display. The Vertical Flight Society, 2022. http://dx.doi.org/10.4050/f-0078-2022-17553.
Texto completoHoppe, F. y G. Sommer. "Ensemble Learning for Hierarchies of Locally Arranged Models". En The 2006 IEEE International Joint Conference on Neural Network Proceedings. IEEE, 2006. http://dx.doi.org/10.1109/ijcnn.2006.247246.
Texto completoByeon, Yeong-Hyeon, Sung-Bum Pan y Keun-Chang Kwak. "Ensemble Deep Learning Models for ECG-based Biometrics". En 2020 Cybernetics & Informatics (K&I). IEEE, 2020. http://dx.doi.org/10.1109/ki48306.2020.9039871.
Texto completoK, Fahmida Minna y Maya Mohan. "Ensemble Learning Models for Drug Target Interaction Prediction". En 2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC). IEEE, 2022. http://dx.doi.org/10.1109/icaaic53929.2022.9793081.
Texto completoPanyushkin, Georgy y Vitalii Varkentin. "Network Traffic and Ensemble Models in Machine Learning". En 2021 International Conference on Quality Management, Transport and Information Security, Information Technologies (IT&QM&IS). IEEE, 2021. http://dx.doi.org/10.1109/itqmis53292.2021.9642907.
Texto completoE M, Roopa Devi, R. Shanthakumari, R. Rajadevi, Anoj Roshan M, Hari V y Lakshmanan S. "Forecasting Air Quality Pollutants using Ensemble Learning Models". En 2023 2nd International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies (ViTECoN). IEEE, 2023. http://dx.doi.org/10.1109/vitecon58111.2023.10157087.
Texto completoInformes sobre el tema "ENSEMBLE LEARNING MODELS"
de Luis, Mercedes, Emilio Rodríguez y Diego Torres. Machine learning applied to active fixed-income portfolio management: a Lasso logit approach. Madrid: Banco de España, septiembre de 2023. http://dx.doi.org/10.53479/33560.
Texto completoHart, Carl R., D. Keith Wilson, Chris L. Pettit y Edward T. Nykaza. Machine-Learning of Long-Range Sound Propagation Through Simulated Atmospheric Turbulence. U.S. Army Engineer Research and Development Center, julio de 2021. http://dx.doi.org/10.21079/11681/41182.
Texto completoLasko, Kristofer y Elena Sava. Semi-automated land cover mapping using an ensemble of support vector machines with moderate resolution imagery integrated into a custom decision support tool. Engineer Research and Development Center (U.S.), noviembre de 2021. http://dx.doi.org/10.21079/11681/42402.
Texto completoPettit, Chris y D. Wilson. A physics-informed neural network for sound propagation in the atmospheric boundary layer. Engineer Research and Development Center (U.S.), junio de 2021. http://dx.doi.org/10.21079/11681/41034.
Texto completoPedersen, Gjertrud. Symphonies Reframed. Norges Musikkhøgskole, agosto de 2018. http://dx.doi.org/10.22501/nmh-ar.481294.
Texto completoMaher, Nicola, Pedro DiNezio, Antonietta Capotondi y Jennifer Kay. Identifying precursors of daily to seasonal hydrological extremes over the USA using deep learning techniques and climate model ensembles. Office of Scientific and Technical Information (OSTI), abril de 2021. http://dx.doi.org/10.2172/1769719.
Texto completoDouglas, Thomas y Caiyun Zhang. Machine learning analyses of remote sensing measurements establish strong relationships between vegetation and snow depth in the boreal forest of Interior Alaska. Engineer Research and Development Center (U.S.), julio de 2021. http://dx.doi.org/10.21079/11681/41222.
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